huggingface[patch]: ruff fixes and rules (#31912)

* bump ruff deps
* add more thorough ruff rules
* fix said rules

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py

@@ -1,9 +1,11 @@
+from __future__ import annotations
+
 from typing import Any, Optional
 
 from langchain_core.embeddings import Embeddings
 from pydantic import BaseModel, ConfigDict, Field
 
-from ..utils.import_utils import (
+from langchain_huggingface.utils.import_utils import (
     IMPORT_ERROR,
     is_ipex_available,
     is_optimum_intel_available,
@@ -33,12 +35,13 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
                 model_kwargs=model_kwargs,
                 encode_kwargs=encode_kwargs
             )
+
     """
 
     model_name: str = Field(default=DEFAULT_MODEL_NAME, alias="model")
     """Model name to use."""
     cache_folder: Optional[str] = None
-    """Path to store models. 
+    """Path to store models.
     Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
     model_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Keyword arguments to pass to the Sentence Transformer model, such as `device`,
@@ -46,12 +49,12 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
     encode_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Keyword arguments to pass when calling the `encode` method for the documents of
-    the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`, 
+    the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
     `precision`, `normalize_embeddings`, and more.
     See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
     query_encode_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Keyword arguments to pass when calling the `encode` method for the query of
-    the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`, 
+    the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
     `precision`, `normalize_embeddings`, and more.
     See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
     multi_process: bool = False
@@ -65,24 +68,25 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         try:
             import sentence_transformers  # type: ignore[import]
         except ImportError as exc:
-            raise ImportError(
+            msg = (
                 "Could not import sentence_transformers python package. "
                 "Please install it with `pip install sentence-transformers`."
-            ) from exc
+            )
+            raise ImportError(msg) from exc
 
         if self.model_kwargs.get("backend", "torch") == "ipex":
             if not is_optimum_intel_available() or not is_ipex_available():
-                raise ImportError(
-                    f'Backend: ipex {IMPORT_ERROR.format("optimum[ipex]")}'
-                )
+                msg = f'Backend: ipex {IMPORT_ERROR.format("optimum[ipex]")}'
+                raise ImportError(msg)
 
             if is_optimum_intel_version("<", _MIN_OPTIMUM_VERSION):
-                raise ImportError(
+                msg = (
                     f"Backend: ipex requires optimum-intel>="
                     f"{_MIN_OPTIMUM_VERSION}. You can install it with pip: "
                     "`pip install --upgrade --upgrade-strategy eager "
                     "`optimum[ipex]`."
                 )
+                raise ImportError(msg)
 
             from optimum.intel import IPEXSentenceTransformer  # type: ignore[import]
 
@@ -104,21 +108,21 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     def _embed(
         self, texts: list[str], encode_kwargs: dict[str, Any]
     ) -> list[list[float]]:
-        """
-        Embed a text using the HuggingFace transformer model.
+        """Embed a text using the HuggingFace transformer model.
 
         Args:
             texts: The list of texts to embed.
             encode_kwargs: Keyword arguments to pass when calling the
                 `encode` method for the documents of the SentenceTransformer
-                 encode method.
+                encode method.
 
         Returns:
             List of embeddings, one for each text.
+
         """
         import sentence_transformers  # type: ignore[import]
 
-        texts = list(map(lambda x: x.replace("\n", " "), texts))
+        texts = [x.replace("\n", " ") for x in texts]
         if self.multi_process:
             pool = self._client.start_multi_process_pool()
             embeddings = self._client.encode_multi_process(texts, pool)
@@ -127,16 +131,17 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
             embeddings = self._client.encode(
                 texts,
                 show_progress_bar=self.show_progress,
-                **encode_kwargs,  # type: ignore
+                **encode_kwargs,
             )
 
         if isinstance(embeddings, list):
-            raise TypeError(
+            msg = (
                 "Expected embeddings to be a Tensor or a numpy array, "
                 "got a list instead."
             )
+            raise TypeError(msg)
 
-        return embeddings.tolist()
+        return embeddings.tolist()  # type: ignore[return-type]
 
     def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """Compute doc embeddings using a HuggingFace transformer model.
@@ -146,6 +151,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
 
         Returns:
             List of embeddings, one for each text.
+
         """
         return self._embed(texts, self.encode_kwargs)
 
@@ -157,6 +163,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
 
         Returns:
             Embeddings for the text.
+
         """
         embed_kwargs = (
             self.query_encode_kwargs